Interethnic differences in neuroimaging markers and cognition in Asians, a population-based study
Bibliographic record
Abstract
We examined interethnic differences in the prevalence of neuroimaging markers of cerebrovascular and neurodegenerative disease in 3 major Asian ethnicities (Chinese, Malays, and Indians), as well as their role in cognitive impairment. 3T MRI brain scans were acquired from 792 subjects (mean age: 70.0 ± 6.5years, 52.1% women) in the multi-ethnic Epidemiology of Dementia In Singapore study. Markers of cerebrovascular disease and neurodegeneration were identified. Cognitive performance was evaluated using Mini Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and a neuropsychological assessment. Compared to Chinese, Malays had a higher burden of intracranial stenosis (OR: 2.28. 95%CI: 1.23-4.20) and cortical atrophy (β: -0.60. 95%CI: -0.78, -0.41), while Indians had a higher burden of subcortical atrophy (β: -0.23. 95%CI: -0.40, -0.06). Moreover, Malay and Indian ethnicities were likely to be cognitively impaired (OR for Malays: 3.79. 95%CI: 2.29-6.26; OR for Indians: 2.87. 95%CI: 1.74-4.74) and showed worse performance in global cognition (β for Malays: -0.51. 95%CI: -0.66, -0.37; and Indians: -0.32. 95%CI: -0.47, -0.17). A higher burden of cerebrovascular and neurodegenerative markers were found in Malays and Indians when compared to Chinese. Further research is required to fully elucidate the factors and pathways that contribute to these observed differences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".